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Toward cyber-eco systems: networked sensing, inference and control for ecological and agricultural systems.

Publication Type:

Book Chapter


Handbook of dynamic data driven applications systems,, Springer, Cham., Volume 2, p.397-417 (2023)


Cyber-eco systems, Ecological sensing, Precision agriculture, Sensor/actuator networks, Wireless


New applications are motivating and informing the design of sen-
sor/actuator networks, and, more broadly, distributed intelligent systems. Key
to the success of these systems is the Dynamic Data Driven Application System
(DDDAS) paradigm, characterized by the ability of the system to ingest new
data and in turn steer the collection of that data. Our knowledge of many phys-
ical systems is uncertain, so that sensing and actuation must be mediated by
inference of the structure and parameters of physical-system models. One ap-
plication domain of rapidly growing interest is ecological research and agricul-
tural systems, motivated by the need to understand plant survival and growth
as a function of genetics, environment, and climate. For these applications, we
must develop cyber-eco systems that infer coupled dynamic data-driven pre-
dictive models of plant growth dynamics in response to weather and climate
drivers that allow incorporation of uncertainty. This Chapter describes the
algorithms and system architecture we have developed for this class of cyber-
eco systems, including sensor/actuator node design, site-level networking, data
assimilation, inference, and distributed control. Among its innovations are a
modular, parallel-processing node hardware design allowing real-time process-
ing and heterogeneous nodes, energy-aware hardware/software design, and a
networking protocol that builds in trade-o s between energy conservation and
latency. Our implementations include experimental networks in an Eastern
USA forest environment and an operational distributed system, the Southwest
Experimental Garden Array, consisting of geographically-distributed outdoor
gardens on an elevational gradient of over 1500 m in Arizona, USA. Finally,
we summarize results for fine-scale inference of soil moisture and control of

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